This study proceeded on two paths; to select signi cant crop yield physical sup-port variables among many potential ones to be included in a model via penalized
methods (LASSO, SCAD, H-Likelihood) and to also propose and demonstrate
the excellent performance of higher levels and very recent extensions of the Gen-eralized Linear Models (GLM); Joint Generalized Linear Models (JGLM) and
Hierarchical Generalized Linear Models (HGLM) in the global quest to develop-ing Statistical Models with highest model accuracy. The analyses is be based
on raw data available at the regional Monitoring and Evaluation o ce of the
Linking Farmers to Markets (FtM) project in Tamale - Ghana. Physical support
(Fixed e ect) variables measured include; crop type, Financial Credit, Training,
Study tour, Demonstrative Practicals, Networking Events, Post harvest Equip-ment, Number of farmers in the FBO and Plot size cultivated. Dependent variable
measured is Total Crop Yield whereas the regions and the particular communi-ties were treated as Random variables. After the highly rigorous processes of
data analysis the study concluded that, the H-Likelihood method of penalized
variable selection performs both selection of signi cant variables and estimation
of their coe cients simultaneously with the least penalize cross-validated errors
compared to the SCAD and the LASSO. In modelling the e ects of xed physi-cal support services given to farmer based organizations on crop yield, the GLM
with assumed xed dispersion will not be recommended by this study. The study
concludes that the proposed modelling of both mean and dispersion (Joint-GLM)
improves the quality of the models signi cantly. In the case of both xed and
random e ects, the, HGLM 2 is highly recommended. This study concludes that
the HGLM 2 performs far better, gives a more tting model and improves the
quality of the crop yield models signi cantly. The study recommends that delib-erate e ort be put into strengthening the Agricultural support systems as a form
of strategy for increasing crop production in Northern Ghana.

Description:

A thesis submitted to The Department of Mathematics, Kwame Nkrumah University of Science And Technology in partial fulfilment of the requirement for the degree Of Doctor of Philosophy in Mathematical Statistics, 2015